Matching MRI brain images between patients or mapping patients' MRI slices to the simulated atlas of a brain is key to the automatic registration of MRI of a brain. The ability to match MRI images would also enable such applications as indexing and searching MRI images among multiple patients or selecting images from the region of interest. In this work, we have introduced robustness, accuracy and cumulative distance metrics and methodology that allows us to compare different techniques and approaches in matching brain MRI of different patients or matching MRI brain slice to a position in the brain atlas. To that end, we have used feature detection methods AGAST, AKAZE, BRISK, GFTT, HardNet, and ORB, which are established methods in image processing, and compared them on their resistance to image degradation and their ability to match the same brain MRI slice of different patients. We have demonstrated that some of these techniques can correctly match most of the brain MRI slices of different patients. When matching is performed with the atlas of the human brain, their performance is significantly lower. The best performing feature detection method was a combination of SIFT detector and HardNet descriptor that achieved 93% accuracy in matching images with other patients and only 52% accurately matched images when compared to atlas.
翻译:将病人之间的MRI脑图像匹配为病人之间的MRI脑图象,或将病人的MRI切片与模拟脑部图集相匹配,是大脑磁RI自动注册的关键。匹配MRI图像的能力还将使得在多个病人之间对MRI图像进行索引和搜索,或从感兴趣的区域选择图像等应用。在这项工作中,我们引入了强健、准确和累积的距离度量和方法,使我们能够将不同病人的脑MRI切片与大脑图集中的大脑MRI切片相匹配的不同技术和方法进行比较,或者将MRI切片与大脑图集中的位置相匹配。为此,我们使用了特征检测方法AGAGAST、AKAZE、BRISK、GFTTT、HardNet和ORB,这些方法是图像处理的既定方法,可以比较他们对图像退化的耐受性,以及他们与不同病人的脑MRI切片相匹配的能力。我们已经证明,其中一些技术可以正确匹配大多数病人的脑MRI切片。当与人类大脑图集进行匹配时,其性能大大降低。最佳的特征检测方法是将SIFTFT检测和硬网络52%相匹配的病人结合。